MedPreceptor: Decoupled Process Supervision for Active Clinical Inquiry in Multi-Turn Medical Consultations

Effective medical consultation requires not only diagnostic reasoning over existing evidence, but also actively eliciting crucial clinical information across multi-turn interactions. However, both human clinicians and Medical LLMs frequently exhibit process-level drawbacks during clinical inquiry, such as repetitive questioning, misdirected inquiry, omission of key evidence, and premature diagnostic closure. Existing methods typically address this by fine-tuning the Doctor LLM itself, which tightly couples consultation execution with process supervision and impedes reusability across heterogeneous models and human clinicians. To address this limitation, we introduce MedPreceptor, a decoupled process supervision framework that selectively intervenes in multi-turn consultations while leaving the underlying Doctor LLM unchanged. To ensure robust decision-making, we construct prefix-aligned supervision via a two-stage annotation pipeline that eliminates future information leakage, and develop Decision-Guided Selective On-Policy Distillation (DG-OPD). By selectively routing teacher signals conditioned on intervention decisions, DG-OPD trains the supervisor on trajectories from three Doctor LLMs alongside real doctor-patient dialogues, enabling it to learn both when to intervene and what actionable guidance to provide. In addition, we construct the Clinically Annotated Consultation Intervention Benchmark (CACI), comprising 1,664 expert-annotated consultation states for evaluating intervention timing and the quality of corrective advice. Across five Doctor LLMs on MAQuE, MedPreceptor improves diagnostic accuracy by up to 10.27%; across seven Doctor LLMs on MedDialogRubrics, it increases clinically important information coverage by up to 7.08 %. On CACI, MedPreceptor achieves 66.11% accuracy, 67.70% F1, and a corrective advice quality score of 7.71/10. These results demonstrate that decoupling process supervision from consultation execution offers an effective, generalizable paradigm for advancing active clinical inquiry across diverse medical LLMs. Code is available at https://anonymous.4open.science/r/MedPreceptor-5E1F.