  Knowing if a model will generalize to data 'in the wild' is crucial for safe
deployment. To this end, we study model disagreement notions that consider the
full predictive distribution - specifically disagreement based on Hellinger
distance, Jensen-Shannon and Kullback-Leibler divergence. We find that
divergence-based scores provide better test error estimates and detection rates
on out-of-distribution data compared to their top-1 counterparts. Experiments
involve standard vision and foundation models.
