Depth estimation has significant applications in medical microscopic imaging, as it can compensate for the limitations of surgeons in obtaining accurate depth information due to the sensory restrictions of the surgical microscope. To address the problem of low depth estimation accuracy caused by factors such as dynamic changes in surgical scenes, soft tissues, and small-scale surgical instruments, a novel depth estimation method is proposed to enable cross-level feature cascade in dense regression. By employing a multi-level feature aggregation module, the contextual information from the encoder was propagated to the decoder. A dual-attention guided feature fusion mechanism based on channel selection and branch optimization was designed to enhance the decoding accuracy. To obtain dense depth ground-truth values, an iterative registration strategy with an automated robotic scanning approach was introduced, which optimized the multi-view point cloud registration process from coarse to fine and reconstructed high-precision depth data from simulated scenes. Experimental results show that the proposed depth estimation method achieves a root mean squared error (RMSE) value of 0.00151 and a scale-invariant log error (SILog) value of 0.03039, which surpasses previous state-of-the-art methods and produces a more accurate depth distribution for the tiny surgical instrument tip.