Data-driven quantitative analysis of animal behavior relies critically on precise video segmentation, accurate 3D animal pose estimation, and behavioral pattern interpretation. Three persistent challenges impede accurate pose estimation: cross-view occlusions, perspective-induced domain shifts, and scarcity of 3D annotations data. To address these limitations, we present UCTPose, a weakly supervised multi-view animal pose estimation framework that synergistically integrates uncertainty-aware 2D modeling with confidence-guided 3D triangulation. The core innovations include: UCTPose employs a reparameterized perturbation module that simulates view-dependent feature uncertainties, enhancing confidence calibration for 2D keypoint predictions under conclusion. A geometry-constrained triangulation head that reconstructs 3D poses by incorporating per-joint confidence scores, optimized via reprojection residual loss to enforce spatial consistency. Comprehensive evaluations on three multi-view mice behavioral datasets demonstrate that UCTPose achieves state-of-the-art performance. These results validate UCTPose's superior cross-view generalization and occlusion resilience. The framework provides a robust tool for high-fidelity 3D kinematic profiling of naturalistic animal behaviors, significantly reducing dependency on exhaustive 3D annotations.