Branch prediction is critical to the execution efficiency of high-performance processors and has seen significant advancements. However, existing loop predictors often struggle to accurately track loop iterations, particularly in out-of-order or speculative execution. This paper proposes the Accurate Count Loop Predictor (ACLP), a loop prediction architecture that improves the accuracy of loop iteration tracking in high-performance processors. ACLP employs a dual confidence mechanism to suppress the influence of unstable loop branches and records committed loop branch counts to infer accurate iteration counts. Experimental results show that ACLP reduces mispredictions per kilo instructions (MPKI) by an average of 4.5% compared to a state-of-the-art loop predictor, with a marginal increase in area and power.