This paper proposes an enhanced-alignment measure (E-measure) for evaluating binary foreground maps (FMs). Existing measures often focus on pixel-level or structural similarities, but human vision is sensitive to both global and local details. The E-measure combines local pixel values with image-level mean values in a single term, capturing both image-level statistics and local pixel matching. It outperforms existing measures on four popular datasets using five meta-measures, including application ranking, SOTA vs. generic, SOTA vs. noise, human ranking, and ground-truth switch. The E-measure achieves significant improvements in all these measures, with improvements ranging from 9.08% to 19.65% compared to other popular measures. The E-measure is simple and effective, and it considers both structural information and global shape coverage. It is also efficient and can be used to evaluate binary foreground maps. The paper also introduces a new dataset containing 555 binary foreground maps ranked by humans to examine the consistency between current measures and human judgments. The E-measure is shown to be more accurate than other measures in ranking these maps. The paper also discusses the limitations of existing measures and suggests future work in developing new segmentation models based on the E-measure.This paper proposes an enhanced-alignment measure (E-measure) for evaluating binary foreground maps (FMs). Existing measures often focus on pixel-level or structural similarities, but human vision is sensitive to both global and local details. The E-measure combines local pixel values with image-level mean values in a single term, capturing both image-level statistics and local pixel matching. It outperforms existing measures on four popular datasets using five meta-measures, including application ranking, SOTA vs. generic, SOTA vs. noise, human ranking, and ground-truth switch. The E-measure achieves significant improvements in all these measures, with improvements ranging from 9.08% to 19.65% compared to other popular measures. The E-measure is simple and effective, and it considers both structural information and global shape coverage. It is also efficient and can be used to evaluate binary foreground maps. The paper also introduces a new dataset containing 555 binary foreground maps ranked by humans to examine the consistency between current measures and human judgments. The E-measure is shown to be more accurate than other measures in ranking these maps. The paper also discusses the limitations of existing measures and suggests future work in developing new segmentation models based on the E-measure.