Camouflaged Object Detection (COD) aims to detect objects with similarpatterns (e.g., texture, intensity, colour, etc) to their surroundings, andrecently has attracted growing research interest. As camouflaged objects oftenpresent very ambiguous boundaries, how to determine object locations as well astheir weak boundaries is challenging and also the key to this task. Inspired bythe biological visual perception process when a human observer discoverscamouflaged objects, this paper proposes a novel edge-based reversiblere-calibration network called ERRNet. Our model is characterized by twoinnovative designs, namely Selective Edge Aggregation (SEA) and ReversibleRe-calibration Unit (RRU), which aim to model the visual perception behaviourand achieve effective edge prior and cross-comparison between potentialcamouflaged regions and background. More importantly, RRU incorporates diversepriors with more comprehensive information comparing to existing COD models.Experimental results show that ERRNet outperforms existing cutting-edgebaselines on three COD datasets and five medical image segmentation datasets.Especially, compared with the existing top-1 model SINet, ERRNet significantlyimproves the performance by $\sim$∼6% (mean E-measure) with notably high speed(79.3 FPS), showing that ERRNet could be a general and robust solution for theCOD task.