histopathology images is a fundamental task in computational pathology. It is also a very challenging task due to complex nuclei morphologies, ambiguous boundaries, and staining variations. Existing methods often struggle to precisely delineate overlapping nuclei and handle class imbalance. We introduce WeaveSeg, a novel deep learning model for nuclei instance segmentation that significantly improves segmentation performance via synergistic integration of adaptive spectral feature refinement and iterative contrast-weaving. WeaveSeg features an adaptive spectral detail refinement (SAR) module for multi-scale feature enhancement via adaptive frequency component fusion, and an iterative contrast-weaving (ICW) module that progressively refines features through integrating contrastive attention, decoupled semantic context, and adaptive gating. Furthermore, we introduce a specialized uncertainty loss to explicitly model ambiguous regions, and a novel local contrast-based self-adaptive adjustment mechanism to accommodate dynamic feature distributions. Extensive experiments on MoNuSeg and CoNSeP demonstrate WeaveSeg's SOTA performance over existing models. Code will be publicly available.