Model Uncertainty Estimation Based on a Dual-Module Neural Network Architecture

In recent years, deep learning has achieved remarkable success in various fields, including biology and medicine. However, the interpretability and robustness still face challenges, as erroneous predictions can lead to serious consequences in critical applications. This paper introduces a new framework for estimating model uncertainty that can enhance the decision-making process. Compared to existing uncertainty estimation methods like Bayesian neural networks and Monte Carlo dropout, this paper provides a more direct way to model uncertainty by directly estimating the output variance through a dual-module neural network architecture. Without the need for multiple forward passes or complex Bayesian inference, it can reduce computational intensity. In specific, this framework consists of a prediction module for input processing and an uncertainty module for estimating prediction uncertainty. We demonstrate the efficiency of our method through applications in medical image segmentation and protein-ligand binding affinity prediction. By incorporating model uncertainty estimation, the proposed framework not only improves the accuracy of deep learning models, but also significantly reduces the computational burden which is especially beneficial for processing large datasets.