  Conformal Prediction (CP) is a powerful framework for constructing prediction
sets with guaranteed coverage. However, recent studies have shown that
integrating confidence calibration with CP can lead to a degradation in
efficiency. In this paper, We propose an adaptive approach that considers the
classifier's uncertainty and employs entropy-based reweighting to enhance the
efficiency of prediction sets for conformal classification. Our experimental
results demonstrate that this method significantly improves efficiency.
