The Segment Anything Model (SAM) excels in image segmentation. yet is challenged in multi-organ segmentation, due to the inherent similarities between organ tissues and the substantial variability in organ size, structure, and texture. This paper proposes to guide the adaptation of SAM for multi-organ segmentation, introducing biological priors of organogenesis, where organs arise from specific germ layers and develop with shared early-stage pathways before divergence into unique structures. We present OG-SAM (Organogenesis SAM), a new paradigm that enables organ-wise adaptation. First, we present OrganAdapt (Organ Adaptation) that integrates a biologically inspired hierarchical adaptation module into SAM, where parameter sharing and specialization follow the developmental trajectory of organs. Second, to effectively address variations in organ size, we propose GoF (Generalized Organ-feature Fusion), a mechanism that facilitates organ-specific multiscale feature pyramid fusion, thereby enhancing segmentation accuracy and robustness. OG-SAM functions as a query-based plug-in, seamlessly integrating with SAM. Experiments show that OG-SAM outperforms competing methods, particularly for challenging organ boundaries.