You can get reliable uncertainty estimates using standard loss functions (cross-entropy, MSE) instead of complex Dirichlet objectives—the math shows this works, and it's simpler to implement in practice.
This paper simplifies Evidential Deep Learning (EDL) for uncertainty estimation by replacing complex Dirichlet-based losses with standard losses like cross-entropy, evaluated at the Dirichlet mean. The authors prove this approximation works well when evidence is strong and show it includes softmax as a special case, making uncertainty estimation easier to implement without sacrificing accuracy.