  In this paper, we propose Evidential Conformal Prediction (ECP) method for
image classifiers to generate the conformal prediction sets. Our method is
designed based on a non-conformity score function that has its roots in
Evidential Deep Learning (EDL) as a method of quantifying model (epistemic)
uncertainty in DNN classifiers. We use evidence that are derived from the logit
values of target labels to compute the components of our non-conformity score
function: the heuristic notion of uncertainty in CP, uncertainty surprisal, and
expected utility. Our extensive experimental evaluation demonstrates that ECP
outperforms three state-of-the-art methods for generating CP sets, in terms of
their set sizes and adaptivity while maintaining the coverage of true labels.
