  For safety, AI systems in health undergo thorough evaluations before
deployment, validating their predictions against a ground truth that is assumed
certain. However, this is actually not the case and the ground truth may be
uncertain. Unfortunately, this is largely ignored in standard evaluation of AI
models but can have severe consequences such as overestimating the future
performance. To avoid this, we measure the effects of ground truth uncertainty,
which we assume decomposes into two main components: annotation uncertainty
which stems from the lack of reliable annotations, and inherent uncertainty due
to limited observational information. This ground truth uncertainty is ignored
when estimating the ground truth by deterministically aggregating annotations,
e.g., by majority voting or averaging. In contrast, we propose a framework
where aggregation is done using a statistical model. Specifically, we frame
aggregation of annotations as posterior inference of so-called plausibilities,
representing distributions over classes in a classification setting, subject to
a hyper-parameter encoding annotator reliability. Based on this model, we
propose a metric for measuring annotation uncertainty and provide
uncertainty-adjusted metrics for performance evaluation. We present a case
study applying our framework to skin condition classification from images where
annotations are provided in the form of differential diagnoses. The
deterministic adjudication process called inverse rank normalization (IRN) from
previous work ignores ground truth uncertainty in evaluation. Instead, we
present two alternative statistical models: a probabilistic version of IRN and
a Plackett-Luce-based model. We find that a large portion of the dataset
exhibits significant ground truth uncertainty and standard IRN-based evaluation
severely over-estimates performance without providing uncertainty estimates.
