Large-scale fiber tracking in the images serial-sectioned from material samples is a critical step to analyze physical properties of continuous fiber reinforced composite materials. In serial-section imaging, increasing the sampling sparsity, i.e., the inter-slice intervals, can lead to significant speedups in data collection. However, increasing the sampling sparsity leads to difficulties in tracking large-scale crowded and similar-appearance fibers through the serial-section slices. One way to address this issue is to dynamically adjust the sampling rate by balancing the tracking accuracy with the data collection time. For this purpose, it is necessary to develop methods for estimating the tracking accuracy on the fly, i.e., immediately after tracking is updated on a new serial-section slice. Typical tracking accuracy metrics require ground truths, which are usually constructed by human annotations and unavailable on the fly. In this paper, we present a new approach to evaluate the performance of online largescale fiber tracking without involving the ground truth. Specifically, we explore the local spatial consistency of the fibers between adjacent slices and define a new performance-evaluation metric based on this spatial consistency. A set of experiments on real composite-material images are conducted to illustrate the effectiveness and accuracy of the proposed performance-evaluation metric for large-scale fiber tracking.