Event cameras are neuromorphically inspired sensors that sparsely and asynchronously report brightness changes. Their unique characteristics of high temporal resolution, high dynamic range, and low power consumption make them well-suited for addressing challenges in monocular depth estimation (e.g., high-speed or low-lighting conditions). However, existing methods primarily treat event streams as black-box learning systems, without incorporating prior physical principles. As a result, they become over-parameterized and fail to fully exploit the rich temporal information inherent in event camera data. To address this limitation, we incorporate physical motion principles and propose a high-accuracy monocular depth estimation framework, in which the likelihood of different depth hypotheses is explicitly determined by motion compensation based on the egomotion of the event camera. Specifically, we introduce a Focus Cost Discrimination (FCD) module that assesses edge sharpness as a key indicator of focus, while also integrating spatial context to facilitate more accurate cost estimation. Furthermore, we analyze the noise patterns within our framework and enhance it with a newly proposed Inter-Hypotheses Cost Aggregation (IHCA) module. This module refines the cost volume through trend prediction and multi-scale cost consistency constraints. Extensive experiments on real-world and synthetic datasets demonstrate that our framework achieves a 22% relative reduction in absolute relative error, highlighting its superior accuracy in monocular depth estimation.