Chain-of-thought (CoT) distillation is a common strategy for transferring reasoning abilities from large teacher models to smaller student models, but in medical question answering, a student may produce plausible rationales while locally drifting toward clinically incorrect reasoning paths. This makes final-answer supervision and uniform rationale imitation insufficient for identifying where reasoning fails. We study this problem as local reasoning preference collapse in structured medical CoT distillation and propose Answer-Calibrated Collapse-Aware Preference Distillation (ACPD). ACPD first exposes the student’s local continuation preferences through teacher-guided verbalized candidate construction and measures domain-level collapse with a Local Collapse Margin (LCM). It then selectively repairs collapse-prone domains by contrasting the student’s most preferred teacher-approved and teacher-disapproved continuations. To ensure that local repair improves decision making rather than only rationale form, ACPD further introduces answer calibration, which anchors the full structured reasoning prefix to the correct final choice. Experiments on medical multiple-choice reasoning benchmarks show that ACPD improves both answer accuracy and collapse-sensitive repair metrics over strong distillation baselines while maintaining competitive out-of-distribution generalization. These results suggest that effective medical CoT distillation should not only transfer teacher rationales, but also diagnose and repair where the student’s reasoning preferences locally diverge from medically reliable reasoning paths.