  Medical image segmentation modeling is a high-stakes task where understanding
of uncertainty is crucial for addressing visual ambiguity. Prior work has
developed segmentation models utilizing probabilistic or generative mechanisms
to infer uncertainty from labels where annotators draw a singular boundary.
However, as these annotations cannot represent an individual annotator's
uncertainty, models trained on them produce uncertainty maps that are difficult
to interpret. We propose a novel segmentation representation, Confidence
Contours, which uses high- and low-confidence ``contours'' to capture
uncertainty directly, and develop a novel annotation system for collecting
contours. We conduct an evaluation on the Lung Image Dataset Consortium (LIDC)
and a synthetic dataset. From an annotation study with 30 participants, results
show that Confidence Contours provide high representative capacity without
considerably higher annotator effort. We also find that general-purpose
segmentation models can learn Confidence Contours at the same performance level
as standard singular annotations. Finally, from interviews with 5 medical
experts, we find that Confidence Contour maps are more interpretable than
Bayesian maps due to representation of structural uncertainty.
