Medical anomaly detection is challenged by the scarcity and diversity of abnormal samples and the lack of precise annotations, limiting the scalability of supervised methods. To address this, we propose FMA-GEN, a novel diffusion-based framework for few-shot, multi-conditional controllable medical anomaly generation and detection. Specifically, FMA-GEN first introduces a text-guided anomaly mask generator, allowing the creation of diverse and semantically meaningful masks. These masks, combined with anomaly embeddings and textual prompts, are used to condition the diffusion process, facilitating the synthesis of high-fidelity and controllable anomalous images. To ensure anatomical realism, we further design a boundary-aware blending module that fuses normal and abnormal regions along mask boundaries. Finally, in the downstream detection stage, we develop a semi-supervised learning scheme that leverages the generated samples to enhance anomaly representation. A mask-guided feature mining strategy is employed to highlight discriminative abnormal features while suppressing interference from normal regions. Extensive experiments on the BraTS and LiverCT datasets demonstrate that FMA-GEN generates realistic and diverse anomalies, leading to significant improvements in both anomaly detection and localization tasks. Our code are available at https://github.com/TytopiaAI/FMA-GEN.