Uncertainty-Guided Deep Learning for Reliable Decision Making in High-Risk Domains

Deep neural networks have demonstrated remarkable performance across various domains, yet their deployment in high-risk applications such as medical diagnosis, autonomous vehicles, and financial systems remains challenging due to overconfident predictions and lack of uncertainty quantification. This paper presents a comprehensive Uncertainty-Guided Deep Neural Network (UG-DNN) framework that integrates aleatoric and epistemic uncertainty estimation with calibrated confidence measures to enable reliable decision making in safety-critical scenarios. Our approach combines Bayesian neural networks, Monte Carlo dropout, and deep ensembles with novel temperature-based calibration techniques. We introduce an adaptive thresholding mechanism that routes uncertain predictions to human experts while autonomously processing high-confidence cases. Extensive experiments on medical imaging (ChestX-ray14, ISIC2019), autonomous driving (KITTI, nuScenes), and financial fraud detection (IEEE-CIS) datasets demonstrate that UG-DNN achieves 15.3% improvement in Expected Calibration Error (ECE), 12.7% higher AUROC for uncertainty-based rejection, and 23.4% reduction in critical misclassifications compared to state-of-the-art methods. Our uncertainty-aware framework provides interpretable confidence estimates that align with actual prediction accuracy, enabling trustworthy AI deployment in high-stakes applications.

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