Whole-slide image classification is often slowed by the need to process many high-magnification patches across a gigapixel slide. This paper proposes an uncertainty-based acceleration strategy that mimics pathologists by sending only suspicious high-uncertainty regions to expensive high-resolution analysis while handling most regions at low magnification. The framework reduces inference cost and deployment burden without sacrificing the accuracy needed for computational pathology applications.