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Cardiac auscultation–based diagnostic tools are in growing demand to assist clinical decision-making in emergency situations.A phonocardiogram (PCG) is a non-invasive, low-cost technique used to graphically record heart sounds. Automated analysis of PCG signals has gained more attention for early, accurate detection of cardiovascular diseases (CVD), as it contains relevant information about structural abnormalities and valvular disorders of the heart. Recent advancements in deep learning (DL) have made it possible to automatically learning important representations from PCG signals. Convolutional, recurrent, and hybrid network architectures have demonstrated strong capabilities for modelling complex temporal dynamics and subtle acoustic patterns associated with heart problems. This reduces the need for manually crafted features. However, most learning-based diagnostic systems still rely on deterministic inference, yielding a single predicted outcome for each recording. Given the variability in physiology, signal noise, and borderline pathological patterns, these deterministic predictions may not accurately represent the reliability of the diagnostic decision. Although existing DL models for PCG-based heart disease classification have achieved impressive performances, most of them make deterministic predictions without providing the quantification of their uncertainty. They pay little attention to two main sources of uncertainty, namely limited data and model parameters (epistemic uncertainty) and inherent noise and variability of PCG recordings (aleatoric uncertainty). Consequently, when these models are applied to ambiguous or clinically unreliable cases, they may produce overconfident predictions that lead to incorrect diagnostic decisions. Subtle acoustic cues such as murmurs, splits and transient anomalies in heart sound are clinically important in diagnosis. However, blindly trusting the results from machine learning and DL models can lead to sensitive signal characteristics being lost or misinterpreted. The lack of understanding of how to calculate uncertainty from model predictions by these DL models is inevitable. As they are often represented as ‘black boxes’. Inspired by these drawbacks, this work incorporates uncertainty quantification (UQ) into deep learning–based PCG classification. Uncertainty is a measure of how confident or unsure the model is about its own predictions, especially when the input signal is noisy, ambiguous, or different from what it already shown. To address the issues of overconfident predictions and the reliability of deterministic models in clinical decision-making, we present an uncertainty-aware deep multimodal early fusion network (DMEFNet) to estimate predictive uncertainty from complementary PCG representations. Motivated by various UQ techniques and deep learning models, we propose a hybrid UQ model based on DMEFNet. Building on this, we will first employ different UQ techniques and then estimate the predictive uncertainty of DMEFNet. As we are aware, this work is among the first to systematically consider uncertainty quantification for heart valve disease classification using PCG, thereby making our model more robust and transparent and readily adaptable in clinical practice.