Neural network classifiers trained with cross-entropy loss achieve strong predictive accuracy but lack the capability to provide inherent predictive uncertainty estimates, thus requiring external techniques to obtain these estimates. In addition, softmax scores for the true class can vary substantially across independent training runs, which limits the reliability of uncertainty-based decisions in downstream tasks. Evidential Deep Learning aims to address these limitations by producing uncertainty estimates in a single pass, but evidential training is highly sensitive to design choices including loss formulation, prior regularization, and activation functions. Therefore, this work introduces an alternative Dirichlet parameter estimation strategy by applying a method of moments estimator to ensembles of softmax outputs, with an optional maximum-likelihood refinement step. This ensemble-based construction decouples uncertainty estimation from the fragile evidential loss design while also mitigating the variability of single-run cross-entropy training, producing explicit Dirichlet predictive distributions. Across multiple datasets, we show that the improved stability and predictive uncertainty behavior of these ensemble-derived Dirichlet estimates translate into stronger performance in downstream uncertainty-guided applications such as prediction confidence scoring and selective classification.

翻译：通过交叉熵损失训练的神经网络分类器虽能实现高精度预测，却无法提供内在的预测不确定性估计，因而需要借助外部技术获取此类估计。此外，真实类别的softmax分数在不同独立训练运行中差异显著，这限制了基于不确定性的决策在下游任务中的可靠性。证据深度学习旨在通过单次前向传播生成不确定性估计来解决这些局限性，但证据训练对损失函数形式、先验正则化及激活函数等设计选择高度敏感。为此，本文提出一种替代的狄利克雷参数估计策略——对softmax输出的集成应用矩估计方法，并辅以可选的最大似然精化步骤。这种基于集成的构建方式将不确定性估计与脆弱的证据损失设计解耦，同时缓解了单次运行交叉熵训练的变异性，从而生成显式的狄利克雷预测分布。跨多个数据集的实验表明，这些基于集成导出的狄利克雷估计所展现的改进稳定性和预测不确定性行为，在预测置信度评分与选择性分类等不确定性引导的下游任务中，能转化为更优越的应用性能。