Medical consultation (MC) is an interactive decision-making process that requires dynamically adapting inquiry directions according to evolving patient feedback. Although large language models (LLMs) have demonstrated strong planning capabilities in medical applications, existing LLM-based MC methods typically make turn-level independent inquiry decisions without adaptive control over global inquiry directions, making them prone to inefficient local exploration. To address this limitation, we propose MCPlanner, a hierarchical adaptive planning-based MC framework in which a high-level planner adaptively controls the inquiry direction, while a low-level planner executes inquiry actions under the selected direction. Additionally, MCPlanner introduces expert-guided distillation and a smoothed confidence calibration strategy to improve high-level planning efficiency and diagnostic accuracy. Experimental results on DXY, GMD, and CMD show that MCPlanner achieves best diagnostic accuracies of 95.2%, 90.8%, and 76.1%, respectively. Notably, on CMD, MCPlanner outperforms the strongest baseline by 5.0 percentage points. Code is publicly available at https://github.com/zh-jia/MCPlanner.