Revisiting Neural Activation Coverage for Uncertainty Estimation

Benedikt Franke; Nils Förster; Frank Köster; Asja Fischer; Markus Lange; Arne Raulf

cs.LGApr 24, 2026

Revisiting Neural Activation Coverage for Uncertainty Estimation

Authors: Benedikt Franke, Nils Förster, Frank Köster, Asja Fischer, Markus Lange, Arne Raulf

Organizations: DLR Institute for AI Safety and Security, Ulm - Germany · Ruhr University Bochum, Bochum, Germany

Abstract

Neural activation coverage (NAC) is a recently-proposed technique for out-of-distribution detection and generalization. We build upon this promising foundation and extend the method to work as an uncertainty estimation technique for already-trained artificial neural networks in the domain of regression. Our experiments confirm NAC uncertainty scores to be more meaningful than other techniques, e.g. Monte-Carlo Dropout.

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May 26, 2026 · eess.IVJ/K move · Enter open · S save

Measuring Prediction Uncertainty in Neural Cellular Automata

Ario Sadafi, Michael Deutges, Nassir Navab, Carsten Marr

Computational Health Center, Helmholtz Munich, Neuherberg, Germany · Helmholtz AI, Helmholtz Munich, Neuherberg, Germany · Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany · Munich Center for Machine Learning, Munich, Germany · Department of Medicine III, Ludwig-Maximilian-University Hospital, Munich, Germany

Neural cellular automata (NCA) provide a lightweight alternative to encoder-decoder segmentation networks. However, it can be difficult to decide when a prediction should be trusted. Here, we study uncertainty estimation for NCA-based medical image segmentation without modifying the underlying architecture or retraining the model. Our approach is motivated by viewing the NCA as a dynamical system where convergent attractors correspond to confident predictions. Concretely, we propose resilience, a simple measure that leverages the intrinsic iterative structure of NCAs by probing the stability of the final prediction under small perturbations of the automaton state. Predictions that return to the same solution are deemed confident, while those that change substantially are flagged as uncertain. We evaluate uncertainty by its ability to predict segmentation quality using selective prediction metrics (

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Dice@90 and AURC) and ranking metrics (AUROC and AUPRC). Across multiple medical segmentation benchmarks, resilience identifies failure cases more reliably than baselines, improving trust and safety in NCA-based models.

Predictive Uncertainty Neural Cellular Automata Resilience

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Abstract

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