With the widespread application of Internet of Things (IoT) technology in the medical field, real-time collection and transmission of medical images become feasible. However, existing privacy-preserving image retrieval schemes often suffer from low efficiency and high communication overhead when operated in resource-constrained IoT environments due to the lack of efficient models. Thus, achieving efficient and secure medicalimage retrieval on limited-resource devices has emerged as a critical challenge. To address this, we propose LPPUBR, a lightweight, privacy-preserving, unsupervised bitmap retrieval scheme designed for IoT environments with constrained resources. LPPUBR utilizes a secure and lightweight deep learning model to extract deep feature descriptors from images and employs product quantization (PQ) to encode them into binary bitmaps, enhancing retrieval efficiency while reducing computational and storage costs. Particularly, a cross-quantization contrastive learning strategy is applied to jointly train the neural network model and PQ codewords for unsupervised learning. Furthermore, to improve interaction efficiency and reduce communication costs among multiple servers, we optimize the intermediate value recovery operation and redesign the related protocols in n-party secret sharing using a group communication strategy. A comprehensive theoretical analysis and experimental evaluation demonstrate that LPPUBR maintains retrieval accuracy comparable to the original unsupervised model while ensuring data security. Moreover, LPPUBR surpasses existing schemes in terms of computational cost, communication overhead, and retrieval efficiency.