Towards Reliable Uncertainty Quantification: When Conformal Prediction Meets Class Incremental Learning

Reliable uncertainty quantification is critical for Class-Incremental Learning (CIL), making Conformal Prediction (CP) a promising approach due to its finite-sample coverage guarantees. However, under a fixed calibration budget, CP must allocate limited calibration samples across classes, while discarded samples from old classes cannot be recovered. As class-wise calibration demands evolve over time, allocations tailored to current CP reliability may become inadequate for future tasks. To address these challenges, we propose BACA, a Budget-Aware Calibration Allocation framework that improves CP reliability in CIL scenarios. Specifically, BACA contains three complementary components: (1) Current Demand Estimation estimates class-wise calibration demand by characterizing the sensitivity of prediction-set instability to calibration size; (2) Irreversibility-Aware Future Protection identifies classes vulnerable to irreversible future risk and protects them from excessive calibration-sample removal; and (3) Marginal Budget Allocation, which reallocates the fixed calibration budget by balancing the marginal stability benefit of adding samples and the marginal instability cost of removing samples. Extensive experiments on three benchmarks with four CIL learners show that BACA consistently improves prediction-set stability under fixed calibration budgets. It achieves higher Jaccard similarity and lower disagreement than uniform allocation while maintaining comparable coverage and average prediction-set size.