Real-time dynamic reconstruction and visualization of endoscopic scenes are critical for advancing digestive medicine applications. Existing methods based on Gaussian Splatting often suffer from geometric inconsistencies under complex occlusions due to inadequate geometric constraints during explicit modeling. To overcome these limitations, we propose Endo2DGS, a novel reconstruction framework that enables efficient 4D dynamic modeling through three key innovations. First, we simplify the 3D Gaussian ellipsoid to a 2D Gaussian ellipse, ensuring robust geometric consistency across multiple views. Second, we introduce learnable deformation parameters for each Gaussian primitive, enabling precise representation of tissue surface deformations. Third, we develop a depth-guided normal estimation approach that enforces geometry-constrained optimization, significantly improving shape recovery in occluded regions. Our framework achieves an optimal balance of visual quality, computational efficiency, and geometric accuracy, demonstrating substantial clinical potential. Experimental results on the EndoNeRF dataset show that Endo2DGS delivers superior visual quality (38.294 PSNR), real-time performance (356.79 FPS), low GPU memory consumption (1.6 GB), and high geometric fidelity (1.875 DMSE).