  Conformal Prediction (CP) quantifies network uncertainty by building a small
prediction set with a pre-defined probability that the correct class is within
this set. In this study we tackle the problem of CP calibration based on a
validation set with noisy labels. We introduce a conformal score that is robust
to label noise. The noise-free conformal score is estimated using the noisy
labeled data and the noise level. In the test phase the noise-free score is
used to form the prediction set. We applied the proposed algorithm to several
standard medical imaging classification datasets. We show that our method
outperforms current methods by a large margin, in terms of the average size of
the prediction set, while maintaining the required coverage.
