The measurement of uncertainty in deep learning prediction

Abstract

Uncertainty estimation is crucial for improving the reliability and robustness of image classification systems, particularly in safety-critical domains such as healthcare and autonomous driving. In this work, we propose a novel Enhanced Multi-Head Evidential Fusion (MCEF) framework that leverages multi-head evidential networks, meta-calibration, and attention-based fusion to provide comprehensive uncertainty quantification while improving predictive accuracy. Our method integrates diverse evidence heads with adaptive calibration and uncertainty-guided attention to capture both aleatoric and epistemic uncertainties, as well as head-level disagreement, enabling more reliable confidence estimation. We validate the proposed method on standard benchmark datasets, including CIFAR- 10, MNIST, and Fashion-MNIST, demonstrating that it consistently outperforms existing relevant methods in terms of classification accuracy while providing rich uncertainty decomposition. The proposed approach not only advances the state of uncertainty-aware image classification but also provides a robust foundation for reliable deployment in real-world applications where accurate predictions and uncertainty awareness are critical.