  Deploying deep learning models in safety-critical applications remains a very
challenging task, mandating the provision of assurances for the dependable
operation of these models. Uncertainty quantification (UQ) methods estimate the
model's confidence per prediction, informing decision-making by considering the
effect of randomness and model misspecification. Despite the advances of
state-of-the-art UQ methods, they are computationally expensive or produce
conservative prediction sets/intervals. We introduce MC-CP, a novel hybrid UQ
method that combines a new adaptive Monte Carlo (MC) dropout method with
conformal prediction (CP). MC-CP adaptively modulates the traditional MC
dropout at runtime to save memory and computation resources, enabling
predictions to be consumed by CP, yielding robust prediction sets/intervals.
Throughout comprehensive experiments, we show that MC-CP delivers significant
improvements over advanced UQ methods, like MC dropout, RAPS and CQR, both in
classification and regression benchmarks. MC-CP can be easily added to existing
models, making its deployment simple.
