Average-case analysis evaluates the expected performance of an algorithm by considering the distribution of all possible inputs and their probabilities. It provides a more realistic assessment of an algorithm's efficiency compared to worst-case analysis, especially when inputs are not uniformly random but follow a specific distribution pattern.