Medical Vision-Language Models (VLMs) are vulnerable to misleading clinical prompts and may follow incorrect user assertions instead of image evidence, a behavior commonly referred to as sycophancy. Existing mitigation methods mainly rely on prompt-level constraints to encourage neutral responses. However, through fine-grained integrated gradient analysis, we observe a phenomenon, although prompting strategies can correct the final prediction, the internal multimodal representations remain biased toward text tokens, while visual tokens continue to receive limited attention in late fusion layers. To address this issue, we propose HD-AS (Head-Driven Activation Steering), a training-free inference-time intervention framework. HD-AS identifies sycophancy-related attention heads through activation drift analysis under different prompting conditions. During inference, a lightweight isotropic damping operation is applied through a forward pre-hook on the target projection layers, reducing the dominance of text features in the hidden space and restoring the contribution of visual representations. Experiments on two public medical Visual Question Answering (VQA) datasets show that HD-AS consistently reduces the flip rate while maintaining diagnostic accuracy.