Assessing stent expansion on fluoroscopic images during Percutaneous Coronary Intervention (PCI) requires coronary stent segmentation, but achieving consistent performance with a single model is challenging due to low stent visibility and morphological variability. We propose a selective ensemble of five EfficientNet-B7/Feature Pyramid Network (FPN) base models, each trained with BCEDice (binary cross-entropy + Dice) plus one auxiliary loss (clDice, Hausdorff distance, perimeter regularity, TopK, or Wasserstein topological homology) and search-optimized weights. A Convolutional Block Attention Module (CBAM) meta-learner integrates the fluoroscopic image, base model probability maps, and pixel-wise uncertainty through two-phase label-free training (random-fit and nearest-neighbor-fit). Internal five-fold cross-validation (942 frames, 826 patients) achieved a Dice Similarity Coefficient (DSC) of 90.43%, exceeding the best-performing individual base model and hard voting by 1.08 and 0.21 percentage points, respectively. External validation (361 frames, 212 patients) reached a DSC of 83.55%, up to 3.68 percentage points above the lowest-performing base model. This uncertainty-aware framework enables robust stent segmentation without additional annotation, supporting automated stent analysis during PCI.