Conformity Supervised Evidential Deep Learning

While deep neural networks achieve remarkable performance, their tendency to produce overconfident predictions remains a critical issue. Evidential deep learning (EDL) solves this problem by modeling class probabilities with a Dirichlet distribution and quantifying the uncertainty in the model’s predictions. However, conventional EDL lacks an explicit supervision for uncertainty quantification, which leads to a risk of assigning excessive evidence to unreliable samples. In this paper, we propose Conformity Supervised Evidential Deep Learning (CSEDL), a EDL framework that learns class prediction and uncertainty through complementary supervision. Specifically, we introduce an extra uncertainty head and the rank of nonconformity score as a sample difficulty metric to supervise it without additional annotations. Together with the direction head supervised by ground-truth labels, CSEDL forms a dual-head architecture, and the two heads are then combined to reconstruct Dirichlet parameters for final prediction. Extensive experiments on out-of-distribution (OOD) detection, tumor referral prediction, autonomous-driving trajectory risk forecasting, trusted multi-view classification and calibrating large language models (LLMs) indicates that CSEDL improves uncertainty quantification ability across multiple tasks. **Code is available in the supplementary materials.**