reviews: 
Comments: This paper introduces a confidence-aware evaluation framework designed to enhance the alignment between LLM-based assessments and human judgments with specified confidence guarantees. The approach consists of three key components: Confidence-Aware Selective Evaluation, which enables models to recognize uncertainty and refrain from providing judgments when confidence is low; Simulated Annotators, which employ in-context learning to represent varied annotator perspectives and improve model reliability while minimizing evaluation mistakes; and Tiered Cascaded Evaluation, which employs a hierarchy of LLM judges, beginning with economical models and progressing to more powerful alternatives only when necessary. The work demonstrates substantial human alignment improvements and substantially decreases dependence on expensive models such as GPT-4 by effectively deploying smaller models where feasible.
Strengths: The paper makes clear and substantial methodological advancements in strengthening alignment between human and LLM evaluations. The tiered cascaded evaluation strategy deserves special mention, as it thoughtfully incorporates economic considerations when choosing between weaker and stronger LLMs. This represents an exemplary piece of research that combines well-motivated contributions with rigorous execution and thoughtful experimental design.
Weaknesses: Despite the paper's exceptional quality, a few refinements could further strengthen it:

1. While Simulated Annotators demonstrate encouraging outcomes, their computational and resource demands will escalate substantially as N and K grow. Given the notable improvement in ECE achieved, the approach merits consideration, but the paper would benefit from a thorough analysis of computational time and resource requirements relative to conventional methods. Additionally, a more thorough exposition of the Simulated Annotators algorithm would be beneficial, particularly regarding the specific mechanisms for implementing in-context learning.

2. The superior performance of Tiered Cascaded Evaluation is somewhat unsurprising given that the framework fundamentally excludes instances anticipated to have high annotator disagreement. The model demonstrates competence in identifying these challenging observations. A more rigorous comparison would involve filtering out the abstained cases from baseline models to assess their performance under identical coverage constraints. As illustrated in Section 3.3, certain systematic features can predict when the model will abstain. An alternative strategy leveraging these predictive signals to identify difficult cases for targeted multi-model evaluation could provide a more balanced comparison.

3. The findings in Table 7 are particularly noteworthy, showing that even weaker model configurations attain 80.3% human agreement despite 68.3% coverage. This compelling result suggests greater exploration of the abstained subset is warranted. Applying the most sophisticated model to the remaining ~30% of samples would simultaneously enhance overall coverage and final performance metrics, while maintaining substantially lower API expenditure compared to exclusive reliance on GPT-4.

4. The economic efficiency of the framework depends critically on task characteristics. Tables 2, 3, and 8 demonstrate varying model compositions across different evaluative tasks. Tasks where smaller models suffice throughout yield greater cost-effectiveness, whereas tasks requiring frequent escalation to larger models become more expensive. This variability warrants explicit discussion, potentially with illustrations of projected API costs across various model allocation scenarios in Section 3.6.

5. A minor point: the selection of different confidence thresholds (1-α=0.9 in Table 2, 1-α=0.85 in Table 3, and 1-α=0.8 in Table 7) appears inconsistent. Adopting uniform threshold values throughout the manuscript would enhance clarity and comparability.

Overall, this represents an engaging and high-quality contribution to the field.
Recommendation: 10
Reviewer confidence: 5
Soundness/correctness: 3
Clarity: 4