Dermatological diagnosis represents a complex multimodal challenge that requires integrating visual features with specialized clinical knowledge. While vision-language pretraining (VLP) has advanced medical AI, its effectiveness in dermatology is limited by text length constraints and the lack of structured texts. In this paper, we introduce MAKE, a Multi-Aspect Knowledge-Enhanced vision-language pretraining framework for zero-shot dermatological tasks. Recognizing that comprehensive dermatological descriptions require multiple knowledge aspects that exceed standard text constraints, our framework introduces: (1) a multi-aspect contrastive learning strategy that decomposes clinical narratives into knowledge-enhanced subtexts through large language models, (2) a fine-grained alignment mechanism that connects subtexts with diagnostically relevant image features, and (3) a diagnosis-guided weighting scheme that adaptively prioritizes different subtexts based on clinical significance prior. Through pretraining on 403,563 dermatological image-text pairs collected from the internet, MAKE significantly outperforms state-of-the-art VLP models on seven datasets across zero-shot skin disease classification, concept annotation, and cross-modal retrieval tasks. Our code is available at https://github.com/SiyuanYan1/MAKE .